Molecular simulation (MD) is a crucial research domain within the life sciences, focusing on comprehending the mechanisms of biomolecular interactions at atomic scales. Protein simulation, as a critical subfield, often utilizes MD for implementation, with trajectory data play a pivotal role in drug discovery. The advancement of high-performance computing and deep learning technology becomes popular and critical to predict protein properties from vast trajectory data, posing challenges regarding data features extraction from the complicated simulation data and dimensionality reduction. Simultaneously, it is essential to provide a meaningful explanation of the biological mechanism behind dimensionality. To tackle this challenge, we propose a new unsupervised model named RevGraphVAMP to intelligently analyze the simulation trajectory. This model is based on the variational approach for Markov processes (VAMP) and integrates graph convolutional neural networks and physical constraint optimization to enhance the learning performance. Additionally, we introduce attention mechanism to assess the importance of key interaction region, facilitating the interpretation of molecular mechanism. In comparison to other VAMPNets models, our model showcases competitive performance, improved accuracy in state transition prediction, as demonstrated through its application to two public datasets and the Shank3-Rap1 complex, which is associated with autism spectrum disorder. Moreover, it enhanced dimensionality reduction discrimination across different substates and provides interpretable results for protein structural characterization.

Robust segmentation of large and complex conjoined tree structures in 3-D is a major challenge in computer vision. This is particularly true in computational biology, where we often encounter large data structures in size, but few in number, which poses a hard problem for learning algorithms. We show that merging multiscale opening with geodesic path propagation, can shed new light on this classic machine vision challenge, while circumventing the learning issue by developing an unsupervised visual geometry approach (digital topology/morphometry). The novelty of the proposed MSO-GP method comes from the geodesic path propagation being guided by a skeletonization of the conjoined structure that helps to achieve robust segmentation results in a particularly challenging task in this area, that of artery-vein separation from non-contrast pulmonary computed tomography angiograms. This is an important first step in measuring vascular geometry to then diagnose pulmonary diseases and to develop image-based phenotypes. We first present proof-of-concept results on synthetic data, and then verify the performance on pig lung and human lung data with less segmentation time and user intervention needs than those of the competing methods.

In this report, non-isomerisable analogs of arginine tRNA (Arg-triazole-tRNA) have been synthesized as tools to study tRNA-dependent aminoacyl-transferases. The synthesis involves the incorporation of 1,4 substituted-1,2,3 triazole ring to mimic the ester bond that connects the amino acid to the terminal adenosine in the natural substrate. The synthetic procedure includes (i) a coupling between 2′- or 3′-azido-adenosine derivatives and a cytidine phosphoramidite to access dinucleotide molecules, (ii) Cu-catalyzed cycloaddition reactions between 2′- or 3′-azido dinucleotide in the presence of an alkyne molecule mimicking the arginine, providing the corresponding Arg-triazole-dinucleotides, (iii) enzymatic phosphorylation of the 5′-end extremity of the Arg-triazole-dinucleotides with a polynucleotide kinase, and (iv) enzymatic ligation of the 5′-phosphorylated dinucleotides with a 23-nt RNA micro helix that mimics the acceptor arm of arg-tRNA or with a full tRNAarg. Characterization of nucleoside and nucleotide compounds involved MS spectrometry, 1H, 13C and 31P NMR analysis. This strategy allows to obtain the pair of the two stable regioisomers of arg-tRNA analogs (2′ and 3′) which are instrumental to explore the regiospecificity of arginyl transferases enzyme. In our study, a first binding assay of the arg-tRNA micro helix with the Arginyl-tRNA-protein transferase 1 (ATE1) was performed by gel shift assays.

Studying the molecular and immunological basis of allergic diseases often requires purified native allergens. The methodologies for protein purification are usually difficult and may not be completely successful. The objective of this work was to describe a methodology to purify allergens from their natural source, while maintaining their native form. The purification strategy consists of a three-step protocol and was used for purifying five specific allergens, Ole e 1, Amb a 1, Alt a 1, Bet v 1 and Cup a 1.Total proteins were extracted in PBS (pH 7.2). Then, the target allergens were pre-purified and enriched by salting-out using increasing concentrations of ammonium sulfate. The allergens were further purified by anion exchange chromatography. Purification of Amb a 1 required an extra step of cation exchange chromatography. The detection of the allergens in the fractions obtained were screened by SDS-PAGE, and Western blot when needed. Further characterization of purified Amb a 1 was performed by mass spectrometry. Ole e 1, Alt a 1, Bet v 1 and Cup a 1 were obtained at > 90 % purity. Amb a 1 was obtained at > 85 % purity.Overall, we propose an easy-to-perform purification approach that allows obtaining highly pure allergens. Since it does not involve neither chaotropic nor organic reagents, we anticipate that the structural and biological functions of the purified molecule remain intact. This method provides a basis for native allergen purification that can be tailored according to specific needs.

DiOlistic labelling is a robust, unbiased ballistic method that utilises lipophilic dyes to morphologically label neurons. While its efficacy on freshly dissected tissue specimens is well-documented, applying DiOlistic labelling to stored, fixed brain tissue and its use in polychromatic multi-marker studies poses significant technical challenges. Here, we present an improved, step-by-step protocol for DiOlistic labelling of dendrites and dendritic spines in fixed mouse tissue. Our protocol encompasses the five key stages: Tissue Preparation, Dye Bullet Preparation, DiOlistic Labelling, Confocal Imaging, and Image Analysis. This method ensures reliable and consistent labelling of dendritic spines in fixed mouse tissue, combined with increased throughput of samples and multi-parameter staining and visualisation of tissue, thereby offering a valuable approach for neuroscientific research.

Metal ions, including biologically prevalent sodium ions, can modulate electrostatic interactions frequently involved in the stability of condensed compartments in cells. Quantitative characterization of heterogeneous ion dynamics inside biomolecular condensates demands new experimental approaches. Here we develop a 23Na NMR relaxation-based integrative approach to probe dynamics of sodium ions inside agarose gels as a model system. We exploit the electric quadrupole moment of spin-3/2 23Na nuclei and, through combination of single-quantum and triple-quantum-filtered 23Na NMR relaxation methods, disentangle the relaxation contribution of different populations of sodium ions inside gels. Three populations of sodium ions are identified: a population with bi-exponential relaxation representing ions within the slow motion regime and two populations with mono-exponential relaxation but at different rates. Our study demonstrates the dynamical heterogeneity of sodium ions inside agarose gels and presents a new experimental approach for monitoring dynamics of sodium and other spin-3/2 ions (e.g. chloride) in condensed environments.

Human leukocyte antigen (HLA) molecules play critically significant role within the realm of immunotherapy due to their capacities to recognize and bind exogenous antigens such as peptides, subsequently delivering them to immune cells. Predicting the binding between peptides and HLA molecules (pHLA) can expedite the screening of immunogenic peptides and facilitate vaccine design. However, traditional experimental methods are time-consuming and inefficient. In this study, an efficient method based on deep learning was developed for predicting peptide-HLA binding, which treated peptide sequences as linguistic entities. It combined the architectures of textCNN and BiLSTM to create a deep neural network model called APEX-pHLA. This model operated without limitations related to HLA class I allele variants and peptide segment lengths, enabling efficient encoding of sequence features for both HLA and peptide segments. On the independent test set, the model achieved Accuracy, ROC_AUC, F1, and MCC is 0.9449, 0.9850, 0.9453, and 0.8899, respectively. Similarly, on an external test set, the results were 0.9803, 0.9574, 0.8835, and 0.7863, respectively. These findings outperformed fifteen methods previously reported in the literature. The accurate prediction capability of the APEX-pHLA model in peptide-HLA binding might provide valuable insights for future HLA vaccine design.

With the recent advanced direct RNA sequencing technique that proposed by the Oxford Nanopore Technologies, RNA modifications can be detected and profiled in a simple and straightforward manner. Majority nanopore-based modification studies were devoted to those popular types such as m6A and pseudouridine. To address current limitations on studying the crucial regulator, m1A modification, we conceived this study. We have developed an integrated computational workflow designed for the detection of m1A modifications from direct RNA sequencing data. This workflow comprises a feature extractor responsible for capturing signal characteristics (such as mean, standard deviations, and length of electric signals), a single molecule-level m1A predictor trained with features extracted from the IVT dataset using classical machine learning algorithms, a confident m1A site selector employing the binomial test to identify statistically significant m1A sites, and an m1A modification rate estimator. Our model achieved accurate molecule-level prediction (Average AUC = 0.9689) and reliable m1A site detection and quantification. To show the feasibility of our workflow, we conducted a study on in vivo transcribed human HEK293 cell line, and the results were carefully annotated and compared with other techniques (i.e., Illumina sequencing-based techniques). We believed that this tool will enabling a comprehensive understanding of the m1A modification and its functional mechanisms within cells and organisms.

The necessity of animal-free performance tests for novel ophthalmic formulation screening is challenging. For this, we developed and validated a new device to simulate the dynamics and physical–chemical barriers of the eye for in vitro performance tests of topic ophthalmic formulations. The OphthalMimic is a 3D-printed device with an artificial lacrimal flow, a cul-de-sac area, a support base, and a simulated cornea comprised of a polymeric membrane containing poly-vinyl alcohol 10 % (w/v), gelatin 2.5 % (w/v), and different proportions of mucin and poloxamer, i.e., 1:1 (M1), 1:2 (M2), and 2:1 (M3) w/v, respectively. The support base is designed to move between 0° and 50° to replicate the movement of an eyelid. We challenged the model by testing the residence performance of poloxamer®407 16 % and poloxamer®407 16 % + chitosan 1 % (PLX16CS10) gels containing fluconazole. The test was conducted with a simulated tear flow of 1.0 mL.min−1 for 5 min. The OphthalMimic successfully distinguished PLX16 and PLX16C10 formulations based on their fluconazole drainage (M1: 65 ± 14 % and 27 ± 10 %; M2: 58 ± 6 % and 38 ± 9 %; M3: 56 ± 5 % and 38 ± 18 %). In conclusion, the OphthalMimic is a promising tool for comparing the animal-free performance of ophthalmic formulations.

Annotating cell types of single-cell RNA sequencing (scRNA-seq) data is crucial for studying cellular heterogeneity in the tumor microenvironment. Recently, large-scale pre-trained language models (PLMs) have achieved significant progress in cell-type annotation of scRNA-seq data. This approach effectively addresses previous methods' shortcomings in performance and generalization. However, fine-tuning PLMs for different downstream tasks demands considerable computational resources, rendering it impractical. Hence, a new research branch introduces parameter-efficient fine-tuning (PEFT). This involves optimizing a few parameters while leaving the majority unchanged, leading to substantial reductions in computational expenses. Here, we utilize scBERT, a large-scale pre-trained model, to explore the capabilities of three PEFT methods in scRNA-seq cell type annotation. Extensive benchmark studies across several datasets demonstrate the superior applicability of PEFT methods. Furthermore, downstream analysis using models obtained through PEFT showcases their utility in novel cell type discovery and model interpretability for potential marker genes. Our findings underscore the considerable potential of PEFT in PLM-based cell type annotation, presenting novel perspectives for the analysis of scRNA-seq data.